SoroushVahidi/scidocs huggingface dataset:
Domain Alignment with Triplets
(2018)

Deep domain adaptation methods can reduce the distribution discrepancy by learning domain-invariant embedddings. However, these methods only focus on aligning the whole data distributions, without considering the classlevel relations among source and target images. Thus, a target embeddings of a bird might be aligned to source embeddings of an airplane. This semantic misalignment can directly degrade the classifier performance on the target dataset. To alleviate this problem, we present a similarity constrained alignment (SCA) method for unsupervised domain adaptation. When aligning the distributions in the embedding space, SCA enforces a similarity-preserving constraint to maintain class-level relations among the source and target images, i.e., if a source image and a target image are of the same class label, their corresponding embeddings are supposed to be aligned nearby, and vise versa. In the absence of target labels, we assign pseudo labels for target images. Given labeled source images and pseudo-labeled target images, the similarity-preserving constraint can be implemented by minimizing the triplet loss. With the joint supervision of domain alignment loss and similarity-preserving constraint, we train a network to obtain domain-invariant embeddings with two critical characteristics, intra-class compactness and inter-class separability. Extensive experiments conducted on the two datasets well demonstrate the effectiveness of SCA.
Sensor fusion for robot control through deep reinforcement learning
(2017)

Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy. In addition to sensors mounted on the robot, sensors might also be deployed in the environment, although these might need to be accessed via an unreliable wireless connection. In this paper, we demonstrate deep neural network architectures that are able to fuse information generated by multiple sensors and are robust to sensor failures at runtime. We evaluate our method on a search and pick task for a robot both in simulation and the real world.
Optimization Criteria, Sensitivity and Robustness of Motion and Structure Estimation
(1999)

The prevailing efforts to study the standard formulation of motion and structure recovery have been recently focused on issues of sensitivity and and robustness of existing techniques. While many cogent observations have been made and verified experimentally, many statements do not hold in general settings and make a comparison of existing techniques difficult. With an ultimate goal of clarifying these issues we study the main aspects of the problem: the choice of objective functions, optimization techniques and the sensitivity and robustness issues in the presence of noise. We clearly reveal the relationship among different objective functions, such as “(normalized) epipolar constraints”, “reprojection error” or “triangulation”, which can all be be unified in a new “ optimal triangulation” procedure formulated as a constrained optimization problem. Regardless of various choices of the objective function, the optimization problems all inherit the same unknown parameter space, the so called “essential manifold”, making the new optimization techniques on Riemanian manifolds directly applicable. Using these analytical results we provide a clear account of sensitivity and robustness of the proposed linear and nonlinear optimization techniques and study the analytical and practical equivalence of different objective functions. The geometric characterization of critical points of a function defined on essential manifold and the simulation results clarify the difference between the effect of bas relief ambiguity and other types of local minima leading to a consistent interpretations of simulation results over large range of signal-to-noise ratio and variety of configurations. Further more we justify the choice of the linear techniques for (re)initialization and for detection of incorrect local minima.
A comparison of loop closing techniques in monocular SLAM
(2009)

Loop closure detection systems for monocular SLAM come in three broad categories: (i) map-to-map, (ii) image-to-image and (iii) image-to-map. In this paper, we have chosen an implementation of each and performed experiments allowing the three approaches to be compared. The sequences used include both indoor and outdoor environments and single and multiple loop trajectories. © 2009 Elsevier B.V. All rights reserved.
A Simplified Model for Normal Mode Helical Antennas
(2010)

─ Normal mode helical antennas are widely used for RFID and mobile communications applications due to their relatively small size and omni-directional radiation pattern. However, their highly curved geometry can make the design and analysis of helical antennas that are part of larger complex structures quite difficult. A simplified model is proposed that replaces the curved helix with straight wires and lumped elements. The simplified model can be used to reduce the complexity of full-wave models that include a helical antenna. It also can be used to estimate the performance of a helical antenna without fullwave modeling of the helical structure. Index Terms─ Helical Antennas, RFID.
Likelihood-informed dimension reduction for nonlinear inverse problems
(2014)

The intrinsic dimensionality of an inverse problem is affected by prior information, the accuracy and number of observations, and the smoothing properties of the forward operator. From a Bayesian perspective, changes from the prior to the posterior may, in many problems, be confined to a relatively lowdimensional subspace of the parameter space. We present a dimension reduction approach that defines and identifies such a subspace, called the “likelihoodinformed subspace” (LIS), by characterizing the relative influences of the prior and the likelihood over the support of the posterior distribution. This identification enables new and more efficient computational methods for Bayesian inference with nonlinear forward models and Gaussian priors. In particular, we approximate the posterior distribution as the product of a lower-dimensional posterior defined on the LIS and the prior distribution marginalized onto the complementary subspace. Markov chain Monte Carlo sampling can then proceed in lower dimensions, with significant gains in computational efficiency. We also introduce a Rao-Blackwellization strategy that de-randomizes Monte Carlo estimates of posterior expectations for additional variance reduction. We demonstrate the efficiency of our methods using two numerical examples: inference of permeability in a groundwater system governed by an elliptic PDE, and an atmospheric remote sensing problem based on Global Ozone Monitoring System (GOMOS) observations.
A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge
(2013)

We present the architecture and the evaluation of a new system for recognizing textual entailment (RTE). In RTE we want to identify automatically the type of a logical relation between two input texts. In particular, we are interested in proving the existence of an entailment between them. We conceive our system as a modular environment allowing for a high-coverage syntactic and semantic text analysis combined with logical inference. For the syntactic and semantic analysis we combine a deep semantic analysis with a shallow one supported by statistical models in order to increase the quality and the accuracy of results. For RTE we use logical inference of first-order employing model-theoretic techniques and automated reasoning tools. The inference is supported with problem-relevant background knowledge extracted automatically and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or other, more experimental sources with, e.g., manually defined presupposition resolutions, or with axiomatized general and common sense knowledge. The results show that fine-grained and consistent knowledge coming from diverse sources is a necessary condition determining the correctness and traceability of results.
Building Consumer Trust Online
(1999)

M oving some Web consumers along to the purchase click is proving to be difficult, despite the impressive recent growth in online shopping. Consumer online shopping revenues and related corporate profits are still meager, though the industry is optimistic, thanks to bullish forecasts of cyberconsumer activity for the new millennium. In 1996, Internet shopping revenues for U.S. users, excluding cars and real estate, were estimated by Jupiter Communications , an e-commerce consulting firm in New York, at approximately $707 million but are expected to How merchants can win back lost consumer trust in the interests of e-commerce sales. reach nearly $37.5 billion by 2002 [1]. Meanwhile, the business-to-business side is taking off with more than $8 billion in revenues for 1997 and $327 billion predicted by 2002 just in the U.S., according to For-rester Research, an information consulting firm in Cambridge, Mass. [4]. On the consumer side, a variety of barriers are invoked to explain the continuing difficulties. There are, to be sure, numerous barriers. Such factors as the lack of standard technologies for secure payment, and the lack of profitable business models play important roles in the relative dearth of commercial activity by businesses and consumers on the Internet compared to what analysts expect in the near future. Granted, the commercial development of the Web is still in its infancy, so few expect these barriers to commercial development to persist. Still, commercial development of the Web faces a far more formidable barrier—consumers' fear of divulging their personal data—to its ultimate com-mercialization. The reason more people have yet to shop online or even provide information to Web providers in exchange for access to information, is the fundamental lack of faith between most businesses and consumers on the Web today. In essence, consumers simply do not trust most Web providers enough to engage in " relationship exchanges " involving money and personal information with them. Our research reveals that this lack of trust arises from the fact that cyberconsumers feel they lack control over the access that Web merchants have to their
Perception-Guided Multimodal Feature Fusion for Photo Aesthetics Assessment
(2014)

Photo aesthetic quality evaluation is a challenging task in multimedia and computer vision fields. Conventional approaches suffer from the following three drawbacks: 1) the deemphasized role of semantic content that is many times more important than low-level visual features in photo aesthetics; 2) the difficulty to optimally fuse low-level and high-level visual cues in photo aesthetics evaluation; and 3) the absence of a sequential viewing path in the existing models, as humans perceive visually salient regions sequentially when viewing a photo.
 To solve these problems, we propose a new aesthetic descriptor that mimics humans sequentially perceiving visually/semantically salient regions in a photo. In particular, a weakly supervised learning paradigm is developed to project the local aesthetic descriptors (graphlets in this work) into a low-dimensional semantic space. Thereafter, each graphlet can be described by multiple types of visual features, both at low-level and in high-level. Since humans usually perceive only a few salient regions in a photo, a sparsity-constrained graphlet ranking algorithm is proposed that seamlessly integrates both the low-level and the high-level visual cues. Top-ranked graphlets are those visually/semantically prominent graphlets in a photo. They are sequentially linked into a path that simulates the process of humans actively viewing. Finally, we learn a probabilistic aesthetic measure based on such actively viewing paths (AVPs) from the training photos that are marked as aesthetically pleasing by multiple users. Experimental results show that: 1) the AVPs are 87.65% consistent with real human gaze shifting paths, as verified by the eye-tracking data; and 2) our photo aesthetic measure outperforms many of its competitors.
Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
(2015)

Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. In this work we anticipate driving maneuvers a few seconds before they occur. For this purpose we equip a car with cameras and a computing device to capture the driving context from both inside and outside of the car. We propose an Autoregressive Input-Output HMM to model the contextual information alongwith the maneuvers. We evaluate our approach on a diverse data set with 1180 miles of natural freeway and city driving and show that we can anticipate maneuvers 3.5 seconds before they occur with over 80% F1-score in real-time.
Pattern recognition based techniques for fruit sorting : A survey
(2015)

In most of the food industries, farms and fruit markets the quality sorting of the fruits is done manually; only a few automated sorting systems are developed for this. Each fruit changes its color in its life span; including stages not ripe, semi ripped, completely ripped or at the end rotten. Hence, we can predict the quality of the fruits by processing its color images and then applying pattern recognition techniques on those images. By quality of fruit we mean size, ripeness, sweetness, longetivity, diseased/ rotten etc. Some advance technologies which are used for classification of fruits are Infrared Imaging, Magnetic Resonance Imaging, Thermal Sensing, Electronic Nose sensing etc; but these are costlier as compared to color image processing and pattern recognition techniques. This paper summarizes and compares various techniques of Pattern Recognition which are used effectively for fruit sorting. All the fruits have their unique ways of ripening stages; hence the pattern recognition techniques used for one fruit does not necessarily match for the other fruit. This paper reviews the high quality research work done for the quality classification of Apples, Dates, Mangoes, Cranberry, Jatropha, Tomatoes, and some other fruits using pattern recognition techniques. KeywordsClassification & predication, Pattern Recognition, Image Processing, Fruit Quality __________________________________________________*****_________________________________________________
The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations.
(1986)

In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.
USER ACCEPTANCE OF COMPUTER TECHNOLOGY : A COMPARISON OF TWO THEORETICAL MODELS *
(2001)

Computer systems cannot improve organizational performance if they aren't used. Unfortunately, resistance to end-user systems by managers and professionals is a widespread problem. To better predict, explain, and increase user acceptance, we need to better understand why people accept or reject computers. This research addresses the ability to predict peoples' computer acceptance from a measure oftheir intentions, and the ability to explain their intentions in terms oftheir attitudes, subjective norms, perceived usefulness, perceived ease of use, and related variables. In a longitudinal study of 107 users, intentions to use a specific system, measured after a onehour introduction to the system, were correlated 0.35 with system use 14 weeks later. The intentionusage correlation was 0.63 at the end of this time period. Perceived usefulness strongly influenced peoples' intentions, explaining more than half of the variance in intentions at the end of 14 weeks. Perceived ease of use had a small but significant effect on intentions as well, although this effect subsided over time. Attitudes only partially mediated the effects of these beliefs on intentions. Subjective norms had no effect on intentions. These results suggest the possibility of simple but powerful models ofthe determinants of user acceptance, with practical value for evaluating systems and guiding managerial interventions aimed at reducing the problem of underutilized computer technology. (INFORMATION TECHNOLOGY; USER ACCEPTANCE; INTENTION MODELS)
Self-perception: An alternative interpretation of cognitive dissonance phenomena.
(1967)

A theory of self-perception is proposed to provide an alternative interpretation for several of the major phenomena embraced by Festinger's theory of cognitive dissonance and to explicate some of the secondary patterns of data that have appeared in dissonance experiments. It is suggested that the attitude statements which comprise the major dependent variables in dissonance experiments may be regarded as interpersonal judgments in which the observer and the observed happen to be the same individual and that it is unnecessary to postulate an aversive motivational drive toward consistency to account for the attitude change phenomena observed. Supporting experiments are presented, and metatheoretical contrasts between the "radical" behavioral approach utilized and the phenomenological approach typified by dissonance theory are discussed.
Attitudes, satisfaction and usage: Factors contributing to each in the acceptance of information technology
(1999)

This research tests and develops the Technology Acceptance Model (TAM), introduced by Davis (1986), which attempts to explain end users’ attitudes to computing technologies. It introduces several new variables, including compatibility, user characteristics, system rating and the enduser computing satisfaction (EUCS) construct, a surrogate measure for IT success and acceptance. A questionnaire with over seventy items was completed by a large number of users and LISREL, a technique for modelling a system of structural equations, was used to analyse the responses. The output shows the model as a whole ® ts the data very well and indicates signi® cant relationships between variables in the model. These results con® rm that TAM is a valuable tool for predicting attitudes, satisfaction, and usage from beliefs and external variables. They also show that relative advantage of the system contributed most to attitudes and satisfaction. Compatibility (of the system to the task performed) contributed most to usage and was the most important antecedent of the belief variables, including relative advantage.
Cardiorespiratory fitness estimation in free-living using wearable sensors
(2016)

OBJECTIVE
In this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data.


METHODS
Our methods rely on a computational framework able to contextualize heart rate (HR) in free-living, and use context-specific HR as predictor of CRF without need for laboratory tests. In particular, we propose three estimation steps. Initially, we recognize activity primitives using accelerometer and location data. Using topic models, we group activity primitives and derive activities composites. We subsequently rank activity composites, and analyze the relation between ranked activity composites and CRF across individuals. Finally, HR data in specific activity primitives and composites is used as predictor in a hierarchical Bayesian regression model to estimate CRF level from the participant's habitual behavior in free-living.


RESULTS
We show that by combining activity primitives and activity composites the proposed framework can adapt to the user and context, and outperforms other CRF estimation models, reducing estimation error between 10.3% and 22.6% on a study population of 46 participants.


CONCLUSIONS
Our investigation showed that HR can be contextualized in free-living using activity primitives and activity composites and robust CRF estimation in free-living is feasible.
Sindhi Language Processing: A survey
(2017)

In this era of information technology, natural language processing (NLP) has become volatile field because of digital reliance of today's communities. The growth of Internet usage bringing the communities, cultures and languages online. In this regard much of the work has been done of the European and east Asian languages, in the result these languages have reached mature level in terms of computational processing. Despite the great importance of NLP science, still most of the South Asian languages are under developing phase. Sindhi language is one of them, which stands among the most ancient languages in the world. The Sindhi language has a great influence on the large community in Sindh province of Pakistan and some states of India and other countries. But unfortunately, it is at infant level in terms of computational processing, because it has not received such attention of language engineering community, due to its complex morphological structure and scarcity of language resources. Therefore, this study has been carried out in order to summarize the existing work on Sindhi Language Processing (SLP) and to explore future research opportunities, also some potential research problems. This paper will be helpful for the researchers in order to find all the information regarding SLP at one place in a unique way.
Multi-View Representation Learning: A Survey from Shallow Methods to Deep Methods
(2016)

Recently, multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas. This paper introduces several principles for multi-view representation learning: correlation, consensus, and complementarity principles. Consequently, we first review the representative methods and theories of multi-view representation learning based on correlation principle, especially on canonical correlation analysis (CCA) and its several extensions. Then from the viewpoint of consensus and complementarity principles we investigate the advancement of multi-view representation learning that ranges from shallow methods including multi-modal topic learning, multi-view sparse coding, and multi-view latent space Markov networks, to deep methods including multi-modal restricted Boltzmann machines, multi-modal autoencoders, and multi-modal recurrent neural networks. Further, we also provide an important perspective from manifold alignment for multi-view representation learning. Overall, this survey aims to provide an insightful overview of theoretical basis and state-of-the-art developments in the field of multi-view representation learning and to help researchers find the most appropriate tools for particular applications.